In this letter, we propose a pseudo-siamese convolutional neural network architecture that enables to solve the task of identifying corresponding patches in very high-resolution optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a loss function based on binary cross entropy, we achieve a one-hot indication if two patches correspond or not. The network is trained and tested on an automatically generated data set that is based on a deterministic alignment of SAR and optical imagery via previously reconstructed and subsequently coregistered 3-D point clouds. The satellite imag...
This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for ...
A major research area in remote sensing is the problem of multi-sensor data fusion. Especially the c...
Abstract—In recent years, convolutional neural networks (CNNs) have drawn considerable attention for...
In this paper, we investigate making use of a convolutional neural network (CNN) to solve the task o...
Tasks such as the monitoring of natural disasters or the detection of change highly benefit from com...
SAR and optical imagery provide highly complementary information about observed scenes. A combined u...
International audienceDetecting similarities between image patches and measuring their mutual displa...
Improving the geo-localization of optical satellite images is an important pre-processing step for m...
This paper addresses the highly challenging prob-lem of automatically detecting man-made ...
Synthetic aperture radar (SAR) image change detection is a critical yet challenging task in the fiel...
Convolutional Neural Network (CNN) has attracted much at- tention for feature learning and image cl...
Among many improved convolutional neural network (CNN) architectures in the optical image classifica...
Every year, the number of applications relying on information extracted from high-resolution satelli...
Deep Learning has gained much interest recently, probably induced by the re- quirements to learn mor...
Modern spaceborne synthetic aperture radar (SAR) sensors, such as TerraSAR-X/TanDEM-X and COSMO-Sky...
This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for ...
A major research area in remote sensing is the problem of multi-sensor data fusion. Especially the c...
Abstract—In recent years, convolutional neural networks (CNNs) have drawn considerable attention for...
In this paper, we investigate making use of a convolutional neural network (CNN) to solve the task o...
Tasks such as the monitoring of natural disasters or the detection of change highly benefit from com...
SAR and optical imagery provide highly complementary information about observed scenes. A combined u...
International audienceDetecting similarities between image patches and measuring their mutual displa...
Improving the geo-localization of optical satellite images is an important pre-processing step for m...
This paper addresses the highly challenging prob-lem of automatically detecting man-made ...
Synthetic aperture radar (SAR) image change detection is a critical yet challenging task in the fiel...
Convolutional Neural Network (CNN) has attracted much at- tention for feature learning and image cl...
Among many improved convolutional neural network (CNN) architectures in the optical image classifica...
Every year, the number of applications relying on information extracted from high-resolution satelli...
Deep Learning has gained much interest recently, probably induced by the re- quirements to learn mor...
Modern spaceborne synthetic aperture radar (SAR) sensors, such as TerraSAR-X/TanDEM-X and COSMO-Sky...
This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for ...
A major research area in remote sensing is the problem of multi-sensor data fusion. Especially the c...
Abstract—In recent years, convolutional neural networks (CNNs) have drawn considerable attention for...